The device, called a smoke logger, is a small, solar-powered air quality monitor that can be attached to the same collars wildlife biologists already use to track animal movements and physiology. Its design and programming have been released as an open-source package, published alongside the team’s initial results in the journal Methods in Ecology and Evolution, so that researchers anywhere in the world can build their own units. The cost of materials is roughly $100 per logger, a figure that matters enormously in a field where wildlife studies, particularly those involving capturing, collaring, and recapturing animals, are notoriously resource intensive. By making the tool cheap and freely available, the Colorado State team hopes to turn smoke exposure from an unmeasured unknown into a standard data stream in wildlife research, one that can be layered onto the GPS coordinates and heart rate readings that many existing collar studies already collect.

At the technical heart of the device is a low-cost, commercially available particulate sensor of the same type used by the PurpleAir citizen-science monitoring network. These sensors use a laser to count and size airborne particles, focusing on what scientists call particulate matter 2.5, or PM2.5: particles with a diameter of 2.5 micrometers or less. That size threshold is not arbitrary. Particles this small are fine enough to penetrate deep into the lungs and cross into the bloodstream, and they are the components of smoke most strongly linked to cardiovascular and respiratory harm in humans. Measuring PM2.5 concentrations at the level of an individual animal therefore provides a direct, biologically meaningful index of the pollution that animal is actually inhaling, rather than an estimate interpolated from a station many kilometers away.

The engineering choices behind the smoke logger reflect the harsh realities of field deployment. Transmitting data from a collar in remote terrain would require costly satellite or cellular connectivity that simply does not exist in most wildlands, and the energy demands of radio transmission would quickly drain a small battery. The team’s solution is to record and store air quality readings locally every 30 minutes, powered by a small solar panel, and to recover the data later when the collar is retrieved. This makes the device suitable for medium and large animals that can carry the added weight, and it fits naturally into the workflow of long-term wildlife studies in which collars are routinely recovered after months or years. The trade-off is that the data arrives on the timescale of the study rather than in real time, but for questions about chronic exposure and behavioral response, that is often exactly what researchers need.

The first deployments on mule deer produced one of the most striking findings of the project. When the researchers compared readings from the deer-borne sensors with data from the closest official monitoring stations, they found that air quality differed by an order of magnitude, meaning the animals were experiencing conditions either ten times better or ten times worse than the nearest station recorded. Air quality, in other words, varies enormously at the microscale, shaped by local terrain, vegetation, wind patterns, and the animal’s own movements through the landscape. George Wittemyer, the CSU professor who led the project, described that discovery as eye-opening, and it carries a broader implication: without sensors on the animals themselves, any estimate of wildlife smoke exposure drawn from regional monitoring networks is likely to be badly wrong.

That variability cuts both ways, and it hints at one of the most intriguing possibilities raised by the research. If air quality shifts dramatically over short distances, then some parts of a landscape may function as refugia during smoke events, places where animals can find measurably cleaner air while the rest of their range chokes under haze. Adam Parlin, who designed the smoke logger as a postdoctoral researcher in CSU’s Department of Fish, Wildlife and Conservation Biology, pointed to exactly this kind of insight as the tool’s promise. Identifying where animals go and how they adjust their behavior during severe smoke events, from the wildlife’s own perspective, could give conservation managers actionable information about which habitats matter most during fire season and how to protect them.

Not every test animal encountered dangerous smoke during the study period. Deer monitored in Colorado and Utah this year were not exposed to unhealthy levels of wildfire smoke, a reminder that exposure is highly episodic and that multi-year deployments will be needed to capture the worst events. But the team demonstrated that the sensors are sensitive enough to register even minimal pollution spikes from traffic for deer living in urban environments, a sign that the devices can resolve subtle gradients in air quality rather than only extreme events. That sensitivity matters because chronic, low-level exposure may carry health costs that accumulate over time, and because behavioral responses to mild smoke may differ from responses to severe events in ways that only fine-grained data can reveal.

The research team is now scaling up through partnerships. They are collaborating with researchers across the western United States to attach smoke loggers to existing studies tracking cougars, deer, and elk, effectively piggybacking on fieldwork that is already underway rather than launching costly new capture programs. They have also stationed stationary sensors in the habitat of the Sierra Nevada fisher, an endangered small mammal related to weasels, to characterize the air quality within the species’ range. Those results will appear in future publications, but the strategy is clear: by combining animal-borne and stationary sensors, researchers can map both the exposure of individual animals and the broader atmospheric conditions of the landscapes they inhabit.

The implications extend beyond wildlife. The U.S. Environmental Protection Agency monitors air quality in populated areas through its AirNow network, leaving vast rural and wilderness regions effectively unmonitored. Sensors carried by animals roaming those unmonitored spaces could fill gaps in the national picture of smoke exposure, offering health-relevant data for rural communities and people who work outdoors as well as for the animals themselves. In a year when massive smoke plumes have degraded air quality for weeks at a time across North America, a chronic problem that Wittemyer notes now affects everyone, that dual benefit gives the technology an unusually broad appeal for a device built to hang around a deer’s neck.

For land managers, the most practical payoff may come in the planning of prescribed burns. Deliberate, controlled fire is one of the most important tools for reducing fuel loads and limiting the severity of future wildfires, but it generates its own smoke, and managers currently have little way of knowing when or where that smoke actually reaches wildlife. Mark Ditmer, the U.S. Forest Service wildlife ecologist who co-led the project, emphasized that a stronger understanding of smoke exposure could allow burns to be timed and located to meet forest-management goals while avoiding sensitive periods and important habitats. As wildfires grow in frequency, size, and severity, and as smoke becomes a recurring seasonal stressor rather than a rare event, tools like the smoke logger offer something conservation biology has never had before: a way to see the air through an animal’s lungs, and to act on what that view reveals.

Subject of Research: Animal-borne air quality sensors for measuring wildfire smoke exposure in wildlife

Article Title: Novel device will help show how wildfire smoke impacts wildlife

Article References: Novel device will help show how wildfire smoke impacts wildlife. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: wildfire smoke, wildlife, PM2.5, air quality, smoke logger, Colorado State University, mule deer, animal-borne sensors, conservation, prescribed burns, Methods in Ecology and Evolution, open-source technology

Volatile organic compounds, or VOCs, are a broad class of carbon-containing gases that evaporate easily at room temperature. They are emitted by an enormous range of everyday materials, including hairsprays, perfumes, disinfectants, paints, solvents, adhesives and even the building materials that make up homes and offices. Once released into the atmosphere, these compounds do not simply drift away harmlessly. In the presence of sunlight and nitrogen oxides, they participate in photochemical reactions that generate ground-level ozone, a respiratory irritant, and secondary organic aerosols, tiny particles that contribute to smog and haze. Because many VOCs are also associated with indoor air quality problems, they have traditionally been studied inside buildings rather than as major outdoor urban emitters. The new research demonstrates that this framing no longer reflects reality in summertime cities.

The study’s lead field measurements were gathered from a cellular tower using a technique known as eddy covariance, an approach that is rarely deployed in urban air quality research. Eddy covariance combines high-frequency wind measurements with continuous chemical monitoring to calculate the net flux of pollutants moving between the surface and the atmosphere. In essence, by tracking how swirling eddies of air carry chemicals upward from the city, scientists can directly determine emission rates for a wide variety of compounds without relying on indirect estimates. The method demands sophisticated instrumentation and a sampling height well above the surrounding buildings, which is why most urban field studies instead measure pollutant concentrations alone, a strategy that reveals what is in the air but not necessarily where it came from or how quickly it is being released.

By pairing the eddy covariance flux measurements with machine learning tools, the research team was able to disentangle the sources of reactive carbon rising from the urban surface. First author Michael Vermeuel, an assistant professor at Purdue University, emphasized how unusual and revealing this direct approach proved to be. According to the researchers, few field experiments have applied this technique in urban environments, and it exposed a broad chemical fingerprint of emissions that changed substantially between the summer and winter campaigns. In winter, the flux measurements pointed overwhelmingly toward vehicle exhaust and combustion-related sources, consistent with the long-standing picture of urban pollution. In summer, however, the dominant emissions shifted toward volatile chemical products and residential sources, a transition that current regulatory models simply do not capture.

Delphine Farmer, a chemistry professor at Colorado State University who helped lead the research, noted that volatile chemical products, including personal care items, paints, solvents and cleaners, along with building materials themselves, were major emissions from the city in summer. She explained that these sources are important but are not accurately included in the current models that cities and states use to predict and reduce air-quality events. That omission matters because air quality advisories, ozone action days and emissions regulations are all built on emission inventories and models that assume a particular mix of pollution sources. If the true summertime mix is dominated by products and buildings rather than tailpipes, then policies designed around traffic may deliver far less benefit than expected during the very season when ozone pollution is most dangerous to public health.

The underlying reason for this seasonal shift is a story of two converging trends. On one side, vehicles on United States roadways have become dramatically cleaner burning over recent decades, thanks to catalytic converters, improved fuel formulations and increasingly stringent emissions standards. As tailpipe emissions have fallen, other less-studied sources of urban air pollution have grown in relative importance. On the other side, many of these emerging sources are temperature-dependent: their emissions increase as conditions get hotter. Volatile chemical products evaporate more readily in warm weather, and the chemical reactions that transform emitted compounds into ozone and aerosols accelerate with sunlight and heat. Dylan Millet, a professor of atmospheric chemistry at the University of Minnesota and a co-author of the study, explained that because many of these emerging pollution sources increase when it is hotter, they are liable to intensify during heat waves and as the climate continues to warm.

This temperature sensitivity creates a troubling feedback loop at the intersection of air quality and climate change. Heat waves, which are becoming more frequent and more intense across North America, will simultaneously boost evaporative emissions from chemical products and building materials and speed up the atmospheric chemistry that turns those emissions into harmful pollutants. Cities that already struggle with summer ozone episodes could therefore face worsening air quality even if vehicle emissions continue to decline. The researchers caution that because current air quality models do not account for this temperature-dependent pollution, city and state agencies may be missing key information in their decision-making frameworks for managing and regulating air pollution, potentially misjudging both the magnitude of summertime pollution events and the most effective interventions.

The measurement site on Long Island offered an ideal natural laboratory for separating these source categories. The region experiences the full seasonal contrast between cold winters and hot, sunny summers, and its urban landscape blends traffic corridors with dense residential neighborhoods. By conducting identical measurement campaigns in both seasons from the same tower, the team could hold the location constant and isolate the effect of season on emission composition. The wintertime data confirmed the conventional understanding that vehicle emissions are the source of most wintertime air pollution. The summertime data, by contrast, revealed that air pollution largely arose from other sources, including chemical products and residences, fundamentally reshaping the picture of what a polluted summer city actually emits.

The implications extend beyond the New York metropolitan area. Volatile chemical products are used in cities across the country and around the world, and the trend toward cleaner vehicles is a global phenomenon driven by tightening standards everywhere. If the Long Island findings generalize, then urban emission inventories nationwide may systematically undercount the contribution of consumer products, paints, solvents and building materials during warm months. Correcting that gap could change how regulators prioritize pollution controls, potentially shifting attention toward product formulation standards, solvent regulations, building-sector emissions and consumer behavior, alongside the traditional focus on transportation. It could also refine the science behind ozone forecasts, giving public health officials better tools to warn vulnerable populations on high-pollution days.

For the research community, the study also represents a methodological milestone. Direct flux measurements of reactive carbon from an urban surface, combined with machine learning source attribution, offer a template for future campaigns in other cities and climates. As Vermeuel and his colleagues showed, measuring what the city actually exhales, rather than merely sampling what happens to be in the air, can overturn assumptions embedded in decades of modeling practice. As summers grow hotter and the vehicle fleet grows cleaner, the invisible emissions from hairspray bottles, paint cans, disinfectant sprays and building envelopes are poised to become an ever-larger share of the urban pollution burden. Recognizing that reality, the researchers argue, is the first step toward air quality policies that protect public health in the summertime cities of a warming world.

Subject of Research: Seasonal variation in urban volatile organic compound emissions from consumer products and buildings

Article Title: Summertime air pollutants often come from household products and buildings

Article References: Summertime air pollutants often come from household products and buildings. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: air pollution, volatile organic compounds, urban emissions, eddy covariance, ozone, heat waves, climate change, air quality models, consumer products, Colorado State University, Science Advances, secondary organic aerosols

The problem began in the mid-1800s, when large flocks of sheep were driven into the high country to graze. The animals targeted the deep-rooted sedges that formed the backbone of the wet meadow ecosystem, plants that anchor the soil, regulate moisture, and pull carbon dioxide from the atmosphere into the ground. Grazing continued for years after the park was established in 1890, and the pressure proved too severe for the native vegetation to rebound. More than a hundred years after the sheep were removed, the sedges have still not returned on their own, a striking example of what ecologists call an alternative stable state, in which an ecosystem becomes locked into a degraded configuration rather than drifting back to its original condition.

In the place of the sedges, shallow-rooted plants moved in. Without the deep root networks that once threaded through the soil, the ground lost structure and biomass, and exposed patches dried out under the intense mountain sun. Today, more than twenty percent of the meadows’ topsoil remains bare, a figure that underscores how far the system sits from its historical condition. The consequences ripple outward: wet meadows are critical habitat for Yosemite’s wildlife, they filter and store water that feeds downstream systems, and they preserve the open mountain vistas that draw millions of visitors to the park each year.

Perhaps the most consequential loss is invisible to the casual observer. Healthy wet meadow soils are enormous carbon reservoirs, built up over centuries as plant material decomposes slowly in waterlogged, oxygen-poor conditions. When the vegetation that sustains those conditions disappears, the balance tips. Without deep-rooted plants to maintain soil moisture and organic inputs, the soil dries, decomposition accelerates, and stored carbon escapes back into the atmosphere. The meadow shifts from a carbon sink to a carbon source, and as the soil loses its water-holding capacity, the odds of natural recovery shrink with every passing season.

To interrupt that spiral, the Colorado State University team took a direct approach: they planted the dominant native species back into the landscape. Working in Tuolumne Meadows, the researchers put 40,000 native sedge seedlings into the ground and then monitored how the plantings affected the ecosystem over the following years. The strategy rests on a simple ecological logic. Rather than waiting passively for a system that shows no capacity for self-repair, restoration practitioners reintroduce the engineer species, the plant that historically built and maintained the habitat, and allow its biology to rebuild the soil conditions everything else depends on.

After three years of monitoring, the results offered grounds for cautious optimism alongside a realistic assessment of the timeline. The planted sedges were slowly improving soil health, increasing the amount of soil organic matter in the ground around them. That matters because soil organic matter is the currency of meadow recovery: it stores nutrients, holds water, and gives the soil the structure it needs to support a functioning plant community. Every increment of organic matter the sedges add nudges the system back toward the wet, carbon-rich state that defined the meadows before grazing.

Yet the full accounting showed the meadow was still losing carbon overall. Continued decomposition of soil organic matter outweighed the new carbon the young sedges were capturing and storing. In other words, the restoration is working, but the plants are rebuilding a bank account that was drained over 150 years, and deposits are currently smaller than withdrawals. The findings, published in the Journal of Geophysical Research: Biogeosciences under the title Can the Carbon Storage Function of a Degraded Mountain Wet Meadow Be Restored?, make clear that three years of growth cannot undo a century and a half of degradation.

Lead author David Cooper, a senior research scientist emeritus in CSU’s Department of Forest and Rangeland Stewardship, framed the work as a necessary intervention in systems that cannot heal themselves. Many ecosystems do not naturally recover from severe disturbance, he noted, and while a place may look green, it may be severely degraded and in need of restoration. The team’s approach, he said, of planting the species that was and should be the dominant vegetation is the path forward. His estimate for the full repair is measured in generations of restoration effort rather than a few field seasons: it might take 25 to 50 years to fix damage that accumulated over roughly 150 years. I think the meadow can be restored, but these things take time, he said.

The Tuolumne Meadows project carries lessons well beyond Yosemite’s boundaries. Mountain wet meadows around the world have been degraded by grazing, water diversion, and climate-driven drying, and many occupy the same precarious position: green enough to appear healthy, but functionally impaired and bleeding carbon. The CSU study demonstrates that active replanting of dominant native species can begin to reverse soil degradation even in a system that has failed to recover naturally for over a century. It also demonstrates the limits of patience as a strategy. In ecosystems pushed past a threshold, simply removing the original stressor, in this case the sheep, is not enough; the vegetation that maintained the soil must be deliberately restored.

For now, the 40,000 seedlings in Tuolumne Meadows represent a long-term bet on the resilience of a landscape given proper care. If the sedges continue to build soil organic matter year after year, the meadow’s carbon balance will eventually tip back, moisture will return, and the habitat and views that define Yosemite’s high country will be secured for the future. The science says it is possible. The science also says it will demand decades of sustained commitment, monitoring, and continued planting, a reminder that restoring an ecosystem is far easier to damage than to rebuild, and that some of the most important conservation work happens slowly, one seedling at a time.

Subject of Research: Ecological restoration of carbon-storing wet meadows in Yosemite National Park

Article Title: Yosemite’s meadows can be restored, but it will take time and effort

Article References: Yosemite’s meadows can be restored, but it will take time and effort. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Yosemite, Tuolumne Meadows, ecological restoration, wet meadows, sedges, soil organic matter, carbon storage, overgrazing, Colorado State University, biogeosciences, alpine ecosystems, native plants

The research team, led by Michelle Larsen and including McKenna Willis, Valeria Scorza, Sangeeta Rao, and Michael Lappin from the Department of Clinical Sciences and the Center for Companion Animal Studies at Colorado State University, set out with a clear primary objective: to compare Giardia cyst detection results among several traditional fecal flotation techniques and an AI-enabled cyst assay, using fecal samples from shelter dogs with diarrhea. The traditional methods included zinc sulfate centrifugation with expert read, sugar centrifugation with expert read, and direct fluorescent antibody testing, known as DFA, also with expert read. The AI platform under evaluation was the Vetscan Imagyst system manufactured by Zoetis, which is designed for in-clinic use and can detect Giardia cysts in approximately ten minutes using machine-learning algorithms trained to recognize the distinctive morphology of the cysts in digitized images of fecal preparations.

The choice of comparator methods reflects the practical realities of veterinary diagnostics. Centrifugal fecal flotation remains the workhorse of most small animal clinics because it is inexpensive and requires only basic equipment, but its performance varies with the flotation solution used. Zinc sulfate centrifugation is widely regarded as one of the better flotation methods for recovering Giardia cysts, while sugar centrifugation is commonly used for the broader detection of gastrointestinal parasites. DFA, by contrast, uses fluorescently labeled antibodies that bind specifically to Giardia cysts, making it one of the most sensitive detection methods available, but it demands specialized fluorescence microscopy and trained personnel, which places it beyond the reach of most general practices. By anchoring the comparison to DFA and to expert-read flotation, the investigators created a benchmark that any point-of-care technology would have to approach closely to be considered clinically credible.

The study’s headline numbers are instructive. When DFA served as the reference standard, the AI-enabled Giardia cyst assay achieved a sensitivity of 71.4 percent and a specificity of 94.8 percent. Zinc sulfate centrifugation with expert read, measured against the same DFA benchmark, posted an identical sensitivity of 71.4 percent but a perfect specificity of 100 percent. When the two non-DFA methods were compared directly with each other, zinc sulfate centrifugation with expert read versus the AI-enabled cyst assay, the AI system delivered 80.0 percent sensitivity and 89.4 percent specificity. In other words, the machine-driven assay performed in roughly the same diagnostic territory as the expert-performed flotation technique that most clinics already rely on, and neither matched the analytic sensitivity of DFA, which detects cysts that both other methods can miss.

One of the most technically interesting parts of the study involved the use of polymerase chain reaction, or PCR, to adjudicate the discordant results. There were three samples that tested positive by DFA but negative by the AI-enabled Giardia cyst assay, and PCR confirmed that all three of these were genuinely false negatives on the AI side. Conversely, there were three samples that were negative by DFA but positive by the AI-enabled assay, and PCR confirmed that two of these were actually false negatives by DFA, meaning the AI system had correctly flagged infections that the antibody-based reference method had missed. This kind of molecular tie-breaking is a valuable feature of modern diagnostic studies, because it reveals that no single method is infallible and that apparent errors on one platform may in fact reflect limitations of the comparator rather than of the technology being tested.

The investigators also evaluated an AI-enabled fecal ova and oocyst assay, a broader screening mode intended to detect a range of gastrointestinal parasites rather than Giardia specifically, as a Giardia detection tool. This assay produced the lowest sensitivity for Giardia cysts among the methods tested, and the team concluded that it should not be used for Giardia detection, reserving that role for the dedicated Giardia cyst assay mode. The distinction matters operationally: the same imaging platform can run different algorithmic modes optimized for different targets, and the study demonstrates that performance characteristics do not necessarily transfer between modes. A general parasite screen that performs well for hookworm eggs or roundworm ova may not be tuned finely enough to catch Giardia cysts, which are smaller and morphologically subtler than many helminth eggs.

A secondary objective of the study used the same sample set to verify earlier published findings on other common gastrointestinal parasites, comparing AI-enabled parasite detection with sugar centrifugation read by experts. The results for detecting these other parasites, which include hookworms of the genus Ancylostoma, roundworms of the genus Toxocara, the whipworm Trichuris vulpis, and coccidian parasites of the genus Cystoisospora, were similar to those reported in prior studies. This replication is significant because it suggests that the earlier positive evaluations of AI-assisted fecal screening were not artifacts of a particular sample population or laboratory environment, but reflect a reproducible level of performance across different cohorts of diarrheic dogs.

From a veterinary practice standpoint, the implications are straightforward. Most small animal clinics do not employ a parasitology expert, and few have the fluorescence equipment needed to run DFA in house. Samples that require those capabilities are typically shipped to reference laboratories, adding days of delay during which an infected dog may continue to shed cysts into the environment and potentially infect other animals or, in the case of zoonotic concerns around Giardia, pose questions for the household. An AI-enabled system that returns a Giardia result in roughly ten minutes, with agreement comparable to expert centrifugal flotation, offers a way to move accurate parasitological diagnosis into the exam room, enabling same-visit treatment decisions and faster infection control in shelters, boarding facilities, and multi-dog households.

The authors conclude that the overall results from the three principal methods were similar, and they suggest that the AI-enabled Giardia cyst assay could be considered for use in small animal clinics for Giardia and other parasite detection, particularly in settings where a parasite expert or DFA testing is not routinely available. That framing is appropriately measured. The study does not claim that the AI system outperforms expert microscopy or DFA; rather, it demonstrates that the technology achieves clinically acceptable agreement with established methods while removing the dependence on scarce human expertise. For clinics that already have access to skilled microscopists and reference laboratory support, the traditional methods remain fully viable, and DFA retains its place as the most sensitive option when the clinical stakes demand maximal detection.

The study also illustrates the evolving relationship between commercial diagnostic developers and academic veterinary research. Zoetis provided the AI-enabled device and its supplies for the study, and the corresponding author, Michelle Larsen, is currently a full-time employee of Zoetis while completing a master’s program at Colorado State University; the company supplied the instruments but, according to the published declarations, did not influence study design, data analysis, or other portions of the research. Transparency about such arrangements is essential as AI diagnostics proliferate, and the open-access publication allows independent readers to scrutinize the sensitivity and specificity figures for themselves. As machine-learning diagnostic platforms continue to spread through veterinary medicine, studies of this kind, which benchmark them rigorously against expert-read traditional methods and use PCR to resolve discordant results, will be the standard against which their clinical adoption is judged.

Subject of Research: Comparison of AI-enabled and traditional diagnostic methods for detecting Giardia and other enteric parasites in diarrheic dogs

Article Title: Comparison of traditional and artificial intelligence methods for detection of enteric pathogens in feces of dogs with diarrhea

Article References: Larsen, M., Willis, M., Scorza, V., Rao, S., & Lappin, M. (2026). Comparison of traditional and artificial intelligence methods for detection of enteric pathogens in feces of dogs with diarrhea. Parasites & Vectors. https://doi.org/10.1186/s13071-026-07685-7

Image Credits: AI Generated

DOI: 10.1186/s13071-026-07685-7

Keywords: Giardia duodenalis, artificial intelligence, veterinary diagnostics, fecal flotation, direct fluorescent antibody, PCR, dogs, point-of-care testing, intestinal parasites, Parasites & Vectors, Colorado State University, diagnostic sensitivity